Robust parameter design optimization using Kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques
作者：
Highlights：
• Application of Kriging, RBF and RBFNN in robust parameter design.
• These models are superior the quadratic polynomial regression model.
• Kriging model is the most accurate model in comparison with the polynomial regression model.
• We introduced a new approach using multi-objective optimization instead of the mean squared error.
摘要
•Application of Kriging, RBF and RBFNN in robust parameter design.•These models are superior the quadratic polynomial regression model.•Kriging model is the most accurate model in comparison with the polynomial regression model.•We introduced a new approach using multi-objective optimization instead of the mean squared error.
论文关键词：Robust parameter design (RPD),Dual response surface,Surrogate models,Kriging,Multi-objective optimization genetic algorithms (MOGA)
论文评审过程：Available online 12 April 2014.
论文官网地址：https://doi.org/10.1016/j.amc.2014.03.082
